[codex] Add flashinfer TRTLLM backend for diffusion NVFP4 (#22717)

python/sglang/jit_kernel/tests/diffusion/test_diffusion_nvfp4_scaled_mm.py

@@ -5,10 +5,14 @@ import pytest
 import torch
 
 from sglang.jit_kernel.nvfp4 import cutlass_scaled_fp4_mm, scaled_fp4_quant
+from sglang.multimodal_gen.runtime.layers.quantization import (
+    modelopt_quant as diffusion_modelopt_quant,
+)
 from sglang.multimodal_gen.runtime.layers.quantization.modelopt_quant import (
     ModelOptFp4Config,
     ModelOptFp4LinearMethod,
 )
+from sglang.multimodal_gen.runtime.platforms import current_platform
 from sglang.srt.layers.quantization.modelopt_quant import pad_nvfp4_weight
 from sglang.test.ci.ci_register import register_cuda_ci
 
@@ -110,12 +114,29 @@ def _quantize_weight_for_checkpoint(
     return weight_fp4, weight_scale_linear.contiguous()
 
 
+def _set_diffusion_fp4_backend(
+    monkeypatch: pytest.MonkeyPatch, backend: str | None
+) -> None:
+    if backend is None:
+        monkeypatch.delenv(
+            "SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND", raising=False
+        )
+    else:
+        monkeypatch.setenv("SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND", backend)
+
+    current_platform.__class__.get_modelopt_flashinfer_fp4_backend.cache_clear()
+    current_platform.__class__.get_modelopt_fp4_gemm_op.cache_clear()
+    diffusion_modelopt_quant._get_fp4_gemm_op.cache_clear()
+
+
 def _build_layer(
     weight_fp4: torch.Tensor,
     weight_scale_linear: torch.Tensor,
     input_global_scale: torch.Tensor,
     weight_global_scale: torch.Tensor,
-) -> None:
+    *,
+    weight_scale_device: torch.device | str | None = None,
+) -> tuple[ModelOptFp4LinearMethod, torch.nn.Module]:
     output_size, input_size_half = weight_fp4.shape
     input_size = input_size_half * 2
     method = ModelOptFp4LinearMethod(
@@ -142,19 +163,62 @@ def _build_layer(
         (1.0 / weight_global_scale).reshape_as(layer.weight_scale_2)
     )
     layer.weight_scale.data.copy_(weight_scale_linear)
+    if weight_scale_device is not None:
+        layer.weight_scale = torch.nn.Parameter(
+            layer.weight_scale.detach().to(weight_scale_device), requires_grad=False
+        )
 
     method.process_weights_after_loading(layer)
 
-    expected_weight, expected_padding_cols = pad_nvfp4_weight(weight_fp4)
-    expected_scale_shape = (
-        ((output_size + 128 - 1) // 128) * 128,
-        (((input_size // BLOCK_SIZE) + 4 - 1) // 4) * 4,
-    )
+    _, flashinfer_backend = current_platform.get_modelopt_fp4_gemm_op()
+    if flashinfer_backend == "trtllm":
+        expected_weight, _ = pad_nvfp4_weight(
+            weight_fp4, n_alignment=128, k_alignment=0
+        )
+        expected_scale = weight_scale_linear
+        if expected_scale.shape[0] != expected_weight.shape[0]:
+            pad_n = expected_weight.shape[0] - expected_scale.shape[0]
+            expected_scale = torch.nn.functional.pad(expected_scale, (0, 0, 0, pad_n))
 
-    assert torch.equal(layer.weight, expected_weight)
-    assert layer.weight_scale_interleaved.shape == expected_scale_shape
-    assert layer.weight_scale_interleaved.dtype == torch.float8_e4m3fn
-    assert layer.weights_padding_cols == expected_padding_cols
+        expected_padding_cols = 0
+        if expected_scale.shape[1] % 4 != 0:
+            padded_scale_k = ((expected_scale.shape[1] + 4 - 1) // 4) * 4
+            pad_scale_k = padded_scale_k - expected_scale.shape[1]
+            expected_scale = torch.nn.functional.pad(
+                expected_scale, (0, pad_scale_k, 0, 0)
+            )
+            pad_weight_k = pad_scale_k * 8
+            expected_weight = torch.nn.functional.pad(
+                expected_weight, (0, pad_weight_k, 0, 0)
+            )
+            expected_padding_cols = pad_weight_k
+
+        expected_weight = flashinfer.shuffle_matrix_a(
+            expected_weight.view(torch.uint8), 128
+        )
+        expected_scale = (
+            flashinfer.shuffle_matrix_sf_a(expected_scale.view(torch.uint8), 128)
+            .reshape(expected_scale.shape)
+            .view(torch.float8_e4m3fn)
+        )
+
+        assert torch.equal(layer.weight, expected_weight)
+        assert torch.equal(
+            layer.weight_scale_interleaved.view(torch.uint8),
+            expected_scale.view(torch.uint8),
+        )
+        assert layer.weights_padding_cols == expected_padding_cols
+    else:
+        expected_weight, expected_padding_cols = pad_nvfp4_weight(weight_fp4)
+        expected_scale_shape = (
+            ((output_size + 128 - 1) // 128) * 128,
+            (((input_size // BLOCK_SIZE) + 4 - 1) // 4) * 4,
+        )
+
+        assert torch.equal(layer.weight, expected_weight)
+        assert layer.weight_scale_interleaved.shape == expected_scale_shape
+        assert layer.weight_scale_interleaved.dtype == torch.float8_e4m3fn
+        assert layer.weights_padding_cols == expected_padding_cols
     torch.testing.assert_close(
         layer.alpha,
         (1.0 / (input_global_scale * weight_global_scale)).to(torch.float32),
@@ -163,6 +227,7 @@ def _build_layer(
         layer.input_scale_inv,
         input_global_scale.to(torch.float32),
     )
+    return method, layer
 
 
 def _resolve_mode(mode: str):
@@ -170,6 +235,8 @@ def _resolve_mode(mode: str):
         return scaled_fp4_quant, cutlass_scaled_fp4_mm, None
     if mode == "flashinfer2":
         return flashinfer.fp4_quantize, flashinfer.mm_fp4, "cudnn"
+    if mode == "flashinfer_trtllm":
+        return flashinfer.fp4_quantize, flashinfer.mm_fp4, "trtllm"
     raise ValueError(f"Unknown mode: {mode}")
 
 
@@ -177,8 +244,14 @@ def _resolve_mode(mode: str):
     not _nvfp4_supported(),
     reason="Diffusion NVFP4 scaled mm correctness requires Blackwell GPUs",
 )
+@pytest.mark.parametrize(
+    "backend", [None, "flashinfer_trtllm"], ids=["default", "flashinfer_trtllm"]
+)
 @pytest.mark.parametrize("m,n,k", TEST_CASES)
-def test_checkpoint_processing(m: int, n: int, k: int) -> None:
+def test_checkpoint_processing(
+    monkeypatch: pytest.MonkeyPatch, backend: str | None, m: int, n: int, k: int
+) -> None:
+    _set_diffusion_fp4_backend(monkeypatch, backend)
     generator = torch.Generator(device=DEVICE)
     generator.manual_seed(20260404 + m + n + k)
 
@@ -247,5 +320,80 @@ def test_flux2_shape_correctness(mode: str) -> None:
     assert diff < DEEPGEMM_FP4_MAX_DIFF, f"{mode=}, {m=}, {n=}, {k=}, {diff=:.6f}"
 
 
+@pytest.mark.skipif(
+    not _nvfp4_supported(),
+    reason="Diffusion NVFP4 scaled mm correctness requires Blackwell GPUs",
+)
+def test_flux2_shape_correctness_flashinfer_trtllm(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    _set_diffusion_fp4_backend(monkeypatch, "flashinfer_trtllm")
+
+    m, n, k = FLUX2_PROJECTION_SHAPE
+    generator = torch.Generator(device=DEVICE)
+    generator.manual_seed(20260404 + m + n + k + 17)
+
+    x = torch.randn((m, k), device=DEVICE, dtype=DTYPE, generator=generator)
+    weight = torch.randn((n, k), device=DEVICE, dtype=DTYPE, generator=generator)
+    input_global_scale = _make_global_scale(x)
+    weight_global_scale = _make_global_scale(weight)
+    weight_fp4, weight_scale_linear = _quantize_weight_for_checkpoint(
+        weight, weight_global_scale
+    )
+
+    method, layer = _build_layer(
+        weight_fp4, weight_scale_linear, input_global_scale, weight_global_scale
+    )
+    actual = method.apply(layer, x)
+
+    x_fp4, x_scale_swizzled = flashinfer.fp4_quantize(x, input_global_scale)
+    weight_fp4_ref, weight_scale_swizzled = flashinfer.fp4_quantize(
+        weight, weight_global_scale
+    )
+    if x_scale_swizzled.dtype == torch.uint8:
+        x_scale_swizzled = x_scale_swizzled.view(torch.float8_e4m3fn)
+    if weight_scale_swizzled.dtype == torch.uint8:
+        weight_scale_swizzled = weight_scale_swizzled.view(torch.float8_e4m3fn)
+
+    expected = torch.matmul(
+        _dequantize_nvfp4(x_fp4, x_scale_swizzled, input_global_scale),
+        _dequantize_nvfp4(
+            weight_fp4_ref, weight_scale_swizzled, weight_global_scale
+        ).t(),
+    )
+
+    diff = _calc_diff(actual, expected.to(dtype=DTYPE))
+    assert diff < DEEPGEMM_FP4_MAX_DIFF, f"{m=}, {n=}, {k=}, {diff=:.6f}"
+
+
+@pytest.mark.skipif(
+    not _nvfp4_supported(),
+    reason="Diffusion NVFP4 scaled mm correctness requires Blackwell GPUs",
+)
+def test_checkpoint_processing_flashinfer_trtllm_cpu_weight_scale(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    _set_diffusion_fp4_backend(monkeypatch, "flashinfer_trtllm")
+
+    m, n, k = FLUX2_PROJECTION_SHAPE
+    generator = torch.Generator(device=DEVICE)
+    generator.manual_seed(20260413 + m + n + k)
+
+    weight = torch.randn((n, k), device=DEVICE, dtype=DTYPE, generator=generator)
+    input_global_scale = torch.tensor(512.0, device=DEVICE, dtype=torch.float32)
+    weight_global_scale = _make_global_scale(weight)
+    weight_fp4, weight_scale_linear = _quantize_weight_for_checkpoint(
+        weight, weight_global_scale
+    )
+
+    _build_layer(
+        weight_fp4,
+        weight_scale_linear,
+        input_global_scale,
+        weight_global_scale,
+        weight_scale_device="cpu",
+    )
+
+
 if __name__ == "__main__":
     sys.exit(pytest.main([__file__, "-v", "-s"]))

python/sglang/multimodal_gen/envs.py

@@ -55,6 +55,7 @@ if TYPE_CHECKING:
     SGLANG_CACHE_DIT_SECONDARY_TS_ORDER: int = 1
     # model loading
     SGLANG_USE_RUNAI_MODEL_STREAMER: bool = True
+    SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND: str | None = None
     SGLANG_DIFFUSION_VAE_CHANNELS_LAST_3D: bool = False
     SGLANG_USE_ROCM_VAE: bool = False
     SGLANG_USE_ROCM_CUDNN_BENCHMARK: bool = False
@@ -278,7 +279,13 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "SGLANG_USE_RUNAI_MODEL_STREAMER": _lazy_bool(
         "SGLANG_USE_RUNAI_MODEL_STREAMER", "true"
     ),
-    # FlashInfer FP4 GEMM backend for the generic diffusion NVFP4 fallback.
+    # FlashInfer FP4 GEMM backend override for diffusion NVFP4.
+    # Supported values:
+    # - auto
+    # - flashinfer_cudnn
+    # - flashinfer_cutlass
+    # - flashinfer_trtllm
+    # Legacy aliases `cudnn` and `trtllm` are also accepted.
     "SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND": _lazy_str(
         "SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND"
     ),

python/sglang/multimodal_gen/runtime/layers/quantization/modelopt_quant.py

@@ -37,10 +37,15 @@ from sglang.srt.layers.quantization.utils import (
     requantize_with_max_scale,
 )
 from sglang.srt.layers.utils.common import copy_or_rebind_param
-from sglang.srt.utils.common import round_up
+from sglang.srt.utils.common import is_flashinfer_available, round_up
 
 logger = logging.getLogger(__name__)
 
+if is_flashinfer_available():
+    import flashinfer
+else:
+    flashinfer = None
+
 
 @lru_cache(maxsize=1)
 def _get_fp4_quantize_op():
@@ -61,6 +66,14 @@ def _prepare_nvfp4_weight_bytes(
     return ((weight >> 4) | (weight << 4)).contiguous()
 
 
+def _require_flashinfer():
+    if flashinfer is None:
+        raise RuntimeError(
+            "flashinfer is required for the diffusion NVFP4 FlashInfer path."
+        )
+    return flashinfer
+
+
 class ModelOptQuantConfig(QuantizationConfig):
     def __init__(
         self,
@@ -479,8 +492,51 @@ class ModelOptFp4LinearMethod(LinearMethodBase):
 
         w = layer.weight.data
         w_swapped = _prepare_nvfp4_weight_bytes(
-            w, swap_weight_nibbles=self.quant_config.swap_weight_nibbles
+            w,
+            swap_weight_nibbles=getattr(self.quant_config, "swap_weight_nibbles", True),
         )
+
+        _, flashinfer_backend = _get_fp4_gemm_op()
+        if flashinfer_backend == "trtllm":
+            flashinfer_ops = _require_flashinfer()
+
+            weight, _ = pad_nvfp4_weight(w_swapped, n_alignment=128, k_alignment=0)
+            scales = layer.weight_scale
+            if scales.shape[0] != weight.shape[0]:
+                pad_n = weight.shape[0] - scales.shape[0]
+                scales = torch.nn.functional.pad(scales, (0, 0, 0, pad_n))
+
+            scale_k = scales.shape[1]
+            weights_padding_cols = 0
+            if scale_k % 4 != 0:
+                padded_scale_k = round_up(scale_k, 4)
+                pad_scale_k = padded_scale_k - scale_k
+                scales = torch.nn.functional.pad(scales, (0, pad_scale_k, 0, 0))
+                pad_weight_k = pad_scale_k * 8
+                weight = torch.nn.functional.pad(weight, (0, pad_weight_k, 0, 0))
+                weights_padding_cols = pad_weight_k
+
+            epilogue_tile_m = 128
+            shuffled_scale_shape = scales.shape
+            if not weight.is_cuda:
+                weight = weight.cuda()
+            if scales.device != weight.device:
+                scales = scales.to(device=weight.device)
+            weight = flashinfer_ops.shuffle_matrix_a(
+                weight.view(torch.uint8), epilogue_tile_m
+            )
+            scales = (
+                flashinfer_ops.shuffle_matrix_sf_a(
+                    scales.view(torch.uint8), epilogue_tile_m
+                )
+                .reshape(shuffled_scale_shape)
+                .view(torch.float8_e4m3fn)
+            )
+
+            layer.weights_padding_cols = weights_padding_cols
+            copy_or_rebind_param(layer, "weight", weight)
+            copy_or_rebind_param(layer, "weight_scale_interleaved", scales)
+            return
         weight, weights_padding_cols = pad_nvfp4_weight(w_swapped)
         layer.weights_padding_cols = weights_padding_cols
         copy_or_rebind_param(layer, "weight", weight)

python/sglang/multimodal_gen/runtime/platforms/cuda.py

@@ -124,30 +124,38 @@ class CudaPlatformBase(Platform):
     @lru_cache(maxsize=1)
     def get_modelopt_flashinfer_fp4_backend(cls) -> str:
         backend = envs.SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND
+        default_backend = "cudnn" if cls.is_blackwell() else "auto"
         if backend is None:
-            return "cudnn" if cls.is_blackwell() else "auto"
+            return default_backend
 
         backend = backend.lower()
-        if backend not in {"auto", "cudnn"}:
+        backend = {
+            "flashinfer_cudnn": "cudnn",
+            "flashinfer_cutlass": "cutlass",
+            "flashinfer_trtllm": "trtllm",
+            "trtllm": "trtllm",
+            "cudnn": "cudnn",
+            "auto": "auto",
+        }.get(backend, backend)
+        if backend not in {"auto", "cudnn", "cutlass", "trtllm"}:
             logger.warning(
                 "Unsupported SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=%r. "
                 "Falling back to %r.",
                 backend,
-                "cudnn" if cls.is_blackwell() else "auto",
+                default_backend,
             )
-            return "cudnn" if cls.is_blackwell() else "auto"
+            return default_backend
         return backend
 
     @classmethod
     @lru_cache(maxsize=1)
     def get_modelopt_fp4_gemm_op(cls) -> tuple[Callable | None, str | None]:
+        requested_backend = envs.SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND
+        prefer_flashinfer = requested_backend is not None
+
         # TODO: Remove this explicit FlashInfer preference once the sm100 CUTLASS
         # LargeM dispatch grows a validated fallback for Blackwell NVFP4 shapes
         # such as Wan2.2's large-M attention projections.
-        prefer_flashinfer = (
-            envs.SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND is not None
-        )
-
         if prefer_flashinfer:
             try:
                 from flashinfer import mm_fp4 as flashinfer_mm_fp4
@@ -155,8 +163,10 @@ class CudaPlatformBase(Platform):
                 return flashinfer_mm_fp4, cls.get_modelopt_flashinfer_fp4_backend()
             except ImportError:
                 logger.warning(
-                    "SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND is set, "
-                    "but flashinfer.mm_fp4 is unavailable. Falling back to cutlass."
+                    "Requested SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=%r "
+                    "but flashinfer.mm_fp4 is unavailable. Falling back to "
+                    "cutlass.",
+                    requested_backend,
                 )
 
         try:

python/sglang/multimodal_gen/tools/compare_diffusion_trajectory_similarity.py

@@ -23,8 +23,10 @@ Example:
 from __future__ import annotations
 
 import argparse
+import contextlib
 import json
 import math
+import os
 from pathlib import Path
 from typing import Any, Sequence
 
@@ -299,18 +301,7 @@ def build_sampling_kwargs(
     return kwargs
 
 
-def run_variant(
-    *,
-    server_kwargs: dict[str, Any],
-    sampling_kwargs: dict[str, Any],
-):
-    from sglang.multimodal_gen.runtime.entrypoints.diffusion_generator import (
-        DiffGenerator,
-    )
-
-    with DiffGenerator.from_pretrained(local_mode=True, **server_kwargs) as generator:
-        result = generator.generate(sampling_params_kwargs=sampling_kwargs)
-
+def _normalize_single_result(result: Any):
     if isinstance(result, list):
         if len(result) != 1:
             raise ValueError(
@@ -322,6 +313,114 @@ def run_variant(
     return result
 
 
+def _clear_diffusion_fp4_backend_caches() -> None:
+    from sglang.multimodal_gen.runtime.layers.quantization import (
+        modelopt_quant as diffusion_modelopt_quant,
+    )
+    from sglang.multimodal_gen.runtime.platforms import current_platform
+
+    diffusion_modelopt_quant._get_fp4_gemm_op.cache_clear()
+    current_platform.__class__.get_modelopt_fp4_gemm_op.cache_clear()
+    current_platform.__class__.get_modelopt_flashinfer_fp4_backend.cache_clear()
+
+
+@contextlib.contextmanager
+def override_diffusion_fp4_backend(backend: str | None):
+    env_name = "SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND"
+    previous = os.environ.get(env_name)
+
+    if backend is None:
+        os.environ.pop(env_name, None)
+    else:
+        os.environ[env_name] = backend
+
+    _clear_diffusion_fp4_backend_caches()
+    try:
+        yield
+    finally:
+        if previous is None:
+            os.environ.pop(env_name, None)
+        else:
+            os.environ[env_name] = previous
+        _clear_diffusion_fp4_backend_caches()
+
+
+def _extract_total_duration_ms(result: Any) -> float | None:
+    metrics = getattr(result, "metrics", None)
+    if not isinstance(metrics, dict):
+        return None
+    total_duration_ms = metrics.get("total_duration_ms")
+    if total_duration_ms is None:
+        return None
+    return float(total_duration_ms)
+
+
+def run_variant(
+    *,
+    server_kwargs: dict[str, Any],
+    sampling_kwargs: dict[str, Any],
+    fp4_gemm_backend: str | None,
+    warmup_runs: int,
+    measure_runs: int,
+):
+    from sglang.multimodal_gen.runtime.entrypoints.diffusion_generator import (
+        DiffGenerator,
+    )
+
+    if warmup_runs < 0:
+        raise ValueError("warmup_runs must be >= 0.")
+    if measure_runs <= 0:
+        raise ValueError("measure_runs must be >= 1.")
+
+    with override_diffusion_fp4_backend(fp4_gemm_backend):
+        with DiffGenerator.from_pretrained(
+            local_mode=True, **server_kwargs
+        ) as generator:
+            for _ in range(warmup_runs):
+                _normalize_single_result(
+                    generator.generate(sampling_params_kwargs=sampling_kwargs)
+                )
+
+            measured_results = []
+            for _ in range(measure_runs):
+                measured_results.append(
+                    _normalize_single_result(
+                        generator.generate(sampling_params_kwargs=sampling_kwargs)
+                    )
+                )
+
+    final_result = measured_results[-1]
+    generation_times = [float(result.generation_time) for result in measured_results]
+    peak_memories = [float(result.peak_memory_mb) for result in measured_results]
+    total_duration_ms = [
+        duration
+        for duration in (
+            _extract_total_duration_ms(result) for result in measured_results
+        )
+        if duration is not None
+    ]
+
+    return {
+        "result": final_result,
+        "fp4_gemm_backend": fp4_gemm_backend or "default",
+        "warmup_runs": warmup_runs,
+        "measure_runs": measure_runs,
+        "generation_time_s": generation_times[-1],
+        "avg_generation_time_s": sum(generation_times) / len(generation_times),
+        "per_run_generation_time_s": generation_times,
+        "peak_memory_mb": peak_memories[-1],
+        "max_peak_memory_mb": max(peak_memories) if peak_memories else 0.0,
+        "per_run_peak_memory_mb": peak_memories,
+        "total_duration_ms": total_duration_ms[-1] if total_duration_ms else None,
+        "avg_total_duration_ms": (
+            sum(total_duration_ms) / len(total_duration_ms)
+            if total_duration_ms
+            else None
+        ),
+        "per_run_total_duration_ms": total_duration_ms,
+    }
+
+
 def _to_jsonable(result: dict[str, Any]) -> dict[str, Any]:
     return json.loads(json.dumps(result, allow_nan=True))
 
@@ -353,6 +452,22 @@ def main() -> None:
     parser.add_argument("--trajectory-step-index", type=int, default=-1)
     parser.add_argument("--reference-transformer-path")
     parser.add_argument("--candidate-transformer-path")
+    parser.add_argument(
+        "--reference-fp4-gemm-backend",
+        help=(
+            "Optional NVFP4 GEMM backend override for the reference run, e.g. "
+            "'flashinfer_trtllm'."
+        ),
+    )
+    parser.add_argument(
+        "--candidate-fp4-gemm-backend",
+        help=(
+            "Optional NVFP4 GEMM backend override for the candidate run, e.g. "
+            "'flashinfer_trtllm'."
+        ),
+    )
+    parser.add_argument("--warmup-runs", type=int, default=0)
+    parser.add_argument("--measure-runs", type=int, default=1)
     parser.add_argument(
         "--reference-component-path",
         action="append",
@@ -423,23 +538,37 @@ def main() -> None:
         output_dir=str(save_root / "candidate") if save_root else None,
     )
 
-    reference = run_variant(
+    reference_run = run_variant(
         server_kwargs=ref_server_kwargs,
         sampling_kwargs=ref_sampling_kwargs,
+        fp4_gemm_backend=args.reference_fp4_gemm_backend,
+        warmup_runs=args.warmup_runs,
+        measure_runs=args.measure_runs,
     )
-    candidate = run_variant(
+    candidate_run = run_variant(
         server_kwargs=cand_server_kwargs,
         sampling_kwargs=cand_sampling_kwargs,
+        fp4_gemm_backend=args.candidate_fp4_gemm_backend,
+        warmup_runs=args.warmup_runs,
+        measure_runs=args.measure_runs,
     )
+    reference = reference_run["result"]
+    candidate = candidate_run["result"]
 
     result = {
         "model_path": args.model_path,
         "prompt": args.prompt,
         "seed": args.seed,
+        "warmup_runs": args.warmup_runs,
+        "measure_runs": args.measure_runs,
         "server_kwargs": {
             "reference": ref_server_kwargs,
             "candidate": cand_server_kwargs,
         },
+        "backend_overrides": {
+            "reference_fp4_gemm_backend": reference_run["fp4_gemm_backend"],
+            "candidate_fp4_gemm_backend": candidate_run["fp4_gemm_backend"],
+        },
         "sampling_kwargs": {
             "width": args.width,
             "height": args.height,
@@ -449,15 +578,13 @@ def main() -> None:
             "guidance_scale_2": args.guidance_scale_2,
         },
         "reference_generation": {
-            "generation_time_s": reference.generation_time,
-            "peak_memory_mb": reference.peak_memory_mb,
-            "output_file_path": reference.output_file_path,
-        },
+            key: value for key, value in reference_run.items() if key != "result"
+        }
+        | {"output_file_path": reference.output_file_path},
         "candidate_generation": {
-            "generation_time_s": candidate.generation_time,
-            "peak_memory_mb": candidate.peak_memory_mb,
-            "output_file_path": candidate.output_file_path,
-        },
+            key: value for key, value in candidate_run.items() if key != "result"
+        }
+        | {"output_file_path": candidate.output_file_path},
         "trajectory_metrics": summarize_trajectory_metrics(
             reference.trajectory_latents,
             candidate.trajectory_latents,
@@ -484,6 +611,12 @@ def main() -> None:
                 "trajectory_selected_step": result["trajectory_metrics"][
                     "selected_step_index"
                 ],
+                "reference_avg_generation_time_s": result["reference_generation"][
+                    "avg_generation_time_s"
+                ],
+                "candidate_avg_generation_time_s": result["candidate_generation"][
+                    "avg_generation_time_s"
+                ],
                 "trajectory_cosine": selected["cosine_similarity"],
                 "trajectory_mae": selected["mae"],
                 "frame0_psnr_db": frame0["psnr_db"],